1. Field of the Invention
The invention relates to noninvasive determination of analytes in the human body. More particularly, the invention relates to methods of analyte calibration and prediction that adjust for state changes not compensated for with a static calibration model.
2. Description of Related Art
Noninvasive
A spectroscopy based noninvasive analyzer delivers external energy in the form of light to a region of the body where the photons interact with the chemical constituents and physiology of the sampled tissue. A portion of the incident photons are scattered or transmitted out of the body where they are detected. Based upon knowledge of the incident photons and detected photons, the chemical and/or structural basis of the sampled site is elucidated. A distinct advantages of a noninvasive system includes the determination of a chemical constituent concentration in the body without the generation of a biohazard in a pain free manner and with the use of limited consumables. Further the technique allows for multiple analyte concentrations to be determined at one time. Some common examples of noninvasive analyzers are magnetic resonance imaging (MRI), X-rays, pulse oximeters, and noninvasive glucose analyzers. With the exception of X-rays, these determinations are performed with relatively harmless wavelengths of radiation. Examples herein focus on noninvasive glucose concentration determination, but the principles apply to the detection of other analytes, such as fats, proteins, water, and blood or tissue constituents.
Diabetes
Diabetes is a chronic disease that results in improper production and utilization of insulin, a hormone that facilitates glucose uptake into cells. While a precise cause of diabetes is unknown, genetic factors, environmental factors, and obesity appear to play roles. Diabetics have increased risk in three broad categories: cardiovascular heart disease, retinopathy, and neuropathy. Diabetics often have one or more of the following complications: heart disease and stroke, high blood pressure, kidney disease, neuropathy (nerve disease and amputations), retinopathy, diabetic ketoacidosis, skin conditions, gum disease, impotence, and fetal complications. Diabetes is a leading cause of death and disability worldwide. Moreover, diabetes is merely one among a group of disorders of glucose metabolism that also include impaired glucose tolerance, and hyperinsulinemia, or hypoglycemia.
Diabetes Prevalence and Trends
Diabetes is an ever more common disease. The World Health Organization (WHO) estimates that diabetes currently afflicts 154 million people worldwide. There are 54 million people with diabetes living in developed countries. The WHO estimates that the number of people with diabetes will grow to 300 million by the year 2025. In the United States, 15.7 million people or 5.9 percent of the population are estimated to have diabetes. Within the United States, the prevalence of adults diagnosed with diabetes increased by six percent in 1999 and rose by 33 percent between 1990 and 1998. This corresponds to approximately eight hundred thousand new cases every year in America. The estimated total cost to the United States economy alone exceeds $90 billion per year. Diabetes Statistics, National Institutes of Health, Publication No. 98-3926, Bethesda, Md. (November 1997).
Long-term clinical studies show that the onset of diabetes related complications is significantly reduced through proper control of blood glucose concentrations. The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N Eng J of Med , 329:977-86 (1993); U.K. Prospective Diabetes Study (UKPDS) Group, Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes, Lancet, 352:837-853 (1998); and Y. Ohkubo, H. Kishikawa, E. Araki, T. Miyata, S. Isami, S. Motoyoshi, Y. Kojima, N. Furuyoshi, M. Shichizi, Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study, Diabetes Res Clin Pract, 28:103-117 (1995).
A vital element of diabetes management is the self-monitoring of blood glucose concentrations by diabetics in the home environment. However, current monitoring techniques discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis. The Diabetes Control and Complication Trial Research Group, supra. As a result, noninvasive measurement of glucose concentration is identified as a beneficial development for the management of diabetes. Implantable glucose concentration analyzers coupled to an insulin delivery system providing an artificial pancreas are also being pursued.
Sampling Methodology
A wide range of technologies serve to analyze the chemical make-up of the body. These techniques are broadly categorized into two groups, invasive and noninvasive. For the purposes of this document, a technology that acquires any biosample from the body for analysis or if any part of the measuring apparatus penetrates into the body, the technology is referred to as invasive.                Invasive: Some examples of invasive technologies for glucose concentration determination in the body are those that analyze the biosamples of whole blood, serum, plasma, interstitial fluid, and mixtures or selectively sampled components of the aforementioned. Typically, these samples are analyzed with electrochemical, electroenzymatic, and/or colorimetric approaches. For example, enzymatic and colorimetric approaches are used to determine the glucose concentration in interstitial fluid samples.        Noninvasive: A number of approaches for determining the glucose concentration in biosamples have been developed that utilize spectrophotometric technologies. These techniques include: Raman and fluorescence, as well as techniques using light from the ultraviolet through the infrared [ultraviolet (200 to 400 nm), visible (400 to 700 nm), near-IR (700 to 2500 nm or 14,286 to 4000 cm−1), and infrared (2500 to 14,285 nm or 4000 to 700 cm−1)].Noninvasive Glucose Determination        
There exist a number of noninvasive approaches for glucose concentration determination. These approaches vary widely, but have at least two common steps. First, an apparatus is used to acquire a signal from the body without obtaining a biological sample. Second, an algorithm is used to convert this signal into a glucose concentration determination.
One type of noninvasive glucose concentration analyzer is based upon spectra. Typically, a noninvasive apparatus uses some form of spectroscopy to acquire a signal or spectrum of a body part. Utilized spectroscopic techniques include Raman and fluorescence, as well as techniques using light from the ultraviolet through the infrared [ultraviolet (200 to 400 nm), visible (400 to 700 nm), near-IR (700 to 2500 nm or 14,286 to 4000 cm−1), and infrared (2500 to 14,285 nm or 4000 to 700 cm−1)]. A particular range for noninvasive glucose determination in diffuse reflectance mode is about 1100 to 2500 nm or ranges therein. K. Hazen, Glucose Determination in Biological Matrices Using Near-Infrared Spectroscopy, doctoral dissertation, University of Iowa (1995).
Mode
Typically, three modes are used to collect noninvasive spectra: transmittance, transflectance, and/or diffuse reflectance. For example the signal collected, typically being light or a spectrum, is transmitted through a region of the body such as a fingertip, diffusely reflected, or transflected. Transflected here refers to collection of the signal not at the incident point or area (diffuse reflectance), and not at the opposite side of the sample (transmittance), but rather at some point on the body between the transmitted and diffuse reflectance collection areas. For example, transflected light enters the fingertip or forearm in one region and exits in another region typically 0.2 to 5 mm or more away depending on the wavelength used. Thus, light that is strongly absorbed by the body such as light near water absorbance maxima at 1450 or 1950 nm is collected after a small radial divergence and light that is less absorbed such as light near water absorbance minima at 1300, 1600, or 2250 nm is collected at greater radial or transflected distances from the incident photons.
Site
Noninvasive techniques are not limited to using the fingertip as a measurement site. Alternative sites for taking noninvasive measurements include: a hand, finger, palmar region, base of thumb, wrist, dorsal aspect of the wrist, forearm, volar aspect of the forearm, dorsal aspect of the forearm, upper arm, head, earlobe, eye, tongue, chest, torso, abdominal region, thigh, calf, foot, plantar region, and toe.
Instrumentation
While this specification focuses on optical based noninvasive analyzers, it is important to note that noninvasive techniques do not have to be based upon spectroscopy. For example, a bioimpedence meter is considered to be a noninvasive device. Within the context of the invention, any device that reads a signal from the body without penetrating the skin and collecting a biological sample is referred to as a noninvasive glucose analyzer. For example, a bioimpedence meter is a noninvasive device.
Noninvasive glucose concentration determination using a near-infrared analyzer generally involves the illumination with an input element of a small region on the body with near-infrared (NIR) electromagnetic radiation, infrared light in the wavelength range 700 to 2500 nm, or one or more wavelength ranges therein, such as 1100 to 1800 nm. The light is partially absorbed and partially scattered according to its interaction with the constituents of the tissue prior to being reflected back to light collection means optically coupled to a detector or directly to a detector. The detected light contains quantitative information that corresponds to the known interaction of the incident light with components of the body tissue including water, fat, protein, and glucose.
Optical Registration
In an alternate embodiment, the guide provides a means for optical registration. In this embodiment, reflectors or light sensitive elements are placed onto the guide. The optical probe assembly is equipped with light sources and several detectors that allow the position of the guide to be accurately assessed, in either two or three dimensions. In a first configuration, two dimensions (x,y) are assessed and a mechanical stop is used to control the third dimension. In a second configuration, the location of the guide is optically assessed in all three dimensions (x,y,z). Because the position of the guide is constant with respect to the targeted tissue volume, the positional assessment provides accurate information regarding the location of the targeted tissue volume with respect to the optical probe. The registration information provided by such assessment is used to place the tissue site onto the optical probe, or vice versa, through any of the following means:                an operator or user is given a visual or audible signal indicating how to move the tissue site with respect to the optical probe;        a mechanical positioning system is used to position the tissue measurement site with respect to the optical probe; or        a mechanical positioning system is used to position the optical probe onto the tissue measurement site.        
One skilled in the art will appreciate that a magnetic sensing system can also be readily applied for assessment of the location of the guide with respect to the tissue measurement site.
While previously described embodiments of the invention employ structural features to control temperature and humidity at the tissue measurement site passively, an alternative embodiment incorporates an airflow device, such as a small blower, to evaporate moisture from the fiber optic probe, the contact surface, and the tissue measurement site.
A noninvasive glucose concentration analyzer has one or more beam paths from a source to a detector. Light source types include a blackbody source, a tungsten-halogen source, one or more LED's, and one or more laser diodes. For multi-wavelength spectrometers a wavelength selection device is used or a series of optical filters are used for wavelength selection. Wavelength selection devices include one or more gratings, prisms, and wavelength selective filters. Variation of the source such as varying which LED or diode is firing is also used for wavelength selection. Detectors are in the form of one or more single element detectors or one or more arrays or bundles of detectors. Detector types include InGaAs, PbS, PbSe, Si, MCT, or the like. Detector arrays include InGaAs, PbS, PbSe, Si, MCT, or the like. Light collection optics such as fiber optics, lenses, and mirrors are commonly used in various configurations as an output element within a spectrometer to direct light from the source to the detector by way of a sample.
Calibration
Glucose concentration analyzers require calibration. This is true for all types of glucose concentration analyzers such as traditional invasive, alternative invasive, noninvasive, and implantable analyzers. One fact associated with noninvasive glucose concentration analyzers is that they are secondary in nature, that is, they do not measure blood glucose concentrations directly. This means that a primary method is required to calibrate these devices in order to measure blood glucose concentrations properly. Many methods of calibration exist.
One noninvasive technology, near-infrared spectroscopy, requires that a mathematical relationship between an in-vivo near-infrared measurement and the actual blood glucose concentration is developed. This is achieved through the collection of in-vivo NIR measurements with corresponding blood glucose concentrations that have been obtained directly through the use of measurement tools such as a YSI (YSI INCORPORATED, Yellow Springs, Ohio) blood glucose concentration analyzer or any appropriate and accurate traditional invasive reference device such as the THERASENSE FREESTYLE (THERASENSE, INC., Alameda Calif.) glucose concentration analyzer.
For spectrophotometric based analyzers, there are several univariate and multivariate methods that are used to develop the mathematical relationship between the measured signal and the actual blood glucose concentration. However, the basic equation being solved is known as the Beer-Lambert Law. This law states that the strength of an absorbance/reflectance measurement is proportional to the concentration of the analyte which is being measured, as in Equation 1,A=εbC  (1)where A is the absorbance/reflectance measurement at a given wavelength of light, ε is the molar absorptivity associated with the molecule of interest at the same given wavelength, b is the distance that the light travels in the sample, and C is the concentration of the molecule of interest (glucose).
Chemometric calibration techniques extract glucose related signal from the measured spectrum through various methods of signal processing and calibration including one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements known as the calibration set and associated set of reference blood glucose concentrations based upon an analysis of capillary blood, venous blood, or interstitial fluid. Common multivariate approaches requiring an exemplary reference glucose concentration vector for each sample spectrum in a calibration include partial least squares (PLS) and principal component regression (PCR). Many additional forms of calibration or optimization are known to those skilled in the art.
An apparatus for measuring infrared throughput typically includes an energy source emitting infrared energy at multiple wavelengths, an input element, an output element, and a spectrum analyzer. Tissue is irradiated with multiple wavelengths from the input element where at least some of the photons are scattered and absorbed by the tissue. A portion of the photons exit the tissue sample, are collected by the output element, are directed toward a detector, and are detected. The resulting signal is utilized in a model for determining the analyte concentration.
Calibration Maintenance
Multivariate models reduce a complex measurement in a space modeled with a reduced number of factors. Data collected for the creation of the original model is done under a set of conditions. Often this set of conditions changes to the extent that the original model no longer functions adequately. For example, the environmental temperature effects the light collection performance of a spectrometer. In addition to instrumentation and environmental impacts, changes in the sample affect the model. For example, commonly interference concentrations vary outside of those tested or new interferences are introduced. In noninvasive determination of glucose concentration in the body, another key issue with sampling is that the sample is alive and dynamic in nature. This results in updates to the calibration being required.
Prediction
A calibration is used in combination with noninvasive spectra of a subject to determine the analyte concentration of that subject.
Dynamic Properties of Skin
The dynamic properties of skin tissue is an important and largely ignored aspect of noninvasive glucose determinations. At a given measurement site, skin tissue is often assumed to remain static, except for changes in the target analyte and other interfering species. However, variations in the physiological state and fluid distribution of tissue profoundly affect the optical properties of tissue layers and compartments over a relatively short period of time.
Many factors impact the physical and chemical state of skin. These include environmental and physiological factors. A list of such factors includes at least body temperature, environmental temperature, food intake, drug or medicine intake, and applied pressure to a sampling site. An impact on one part of the body will affect many other locations in the body. For example, food intake into the digestive track causes movement of water between internal body compartments. Another example is caffeine or stimulant intake that changes blood pressure or results in dilation of capillaries.
Noninvasive Glucose Determination
There exist a number of reports on noninvasive glucose technologies. Some of these relate to general instrumentation configurations required for noninvasive glucose determination. Others refer to sampling technologies. Those most related to the present invention are briefly reviewed here:
General Instrumentation
P. Rolfe, Investigating substances in a patient's bloodstream, UK Patent Application No. 2,033,575 (Aug. 24, 1979) describe an apparatus for directing light into the body, detecting attenuated backscattered light, and utilizing the collected signal to determine glucose concentrations in or near the bloodstream.
C. Dahne, D. Gross, Spectrophotometric method and apparatus for the non-invasive, U.S. Pat. No. 4,655,225 (Apr. 7, 1987) describe a method and apparatus for directing light into a patient's body, collecting transmitted or backscattered light, and determining glucose from selected near-IR wavelength bands. Wavelengths include 1560 to 1590, 1750 to 1780, 2085 to 2115, and 2255 to 2285 nm with at least one additional reference signal from 1000 to 2700 nm.
M. Robinson, K. Ward, R. Eaton, D. Haaland, Method and apparatus for determining the similarity of a biological analyte from a model constructed from known biological fluids, U.S. Pat. No. 4,975,581 (Dec. 4, 1990) describe a method and apparatus for measuring a concentration of a biological analyte such as glucose using infrared spectroscopy in conjunction with a multivariate model. The multivariate model is constructed form plural known biological fluid samples.
J. Hall, T. Cadell, Method and device for measuring concentration levels of blood constituents non-invasively, U.S. Pat. No. 5,361,758 (Nov. 8, 1994) describe a noninvasive device and method for determining analyte concentrations within a living subject utilizing polychromatic light, a wavelength separation device, and an array detector. The apparatus uses a receptor shaped to accept a fingertip with means for blocking extraneous light.
R. Barnes, J. Brasch, D. Purdy, W. Lougheed, Non-invasive determination of analyte concentration in body of mammals, U.S. Pat. No. 5,379,764 (Jan. 10, 1995) describe a noninvasive glucose analyzer that uses data pretreatment in conjunction with a multivariate analysis to determine blood glucose concentrations.
S. Malin, G Khalil, Method and apparatus for multi-spectral analysis of organic blood analytes in noninvasive infrared spectroscopy, U.S. Pat. No. 6,040,578 Mar. 21, 2000) describe a method and apparatus for determination of an organic blood analyte using multi-spectral analysis in the near-IR. A plurality of distinct nonoverlapping regions of wavelengths are incident upon a sample surface, diffusely reflected radiation is collected, and the analyte concentration is determined via chemometric techniques.
Temperature
It is a well-known that many physiological constituents have near-IR absorbance spectra that are sensitive in terms of magnitude and location to localized temperature. This has been reported as impacting noninvasive glucose determinations. [see K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August, 1995)].
Coupling Fluid
T. Blank, G. Acosta, M. Mattu, S. Monfre, Fiber optic probe guide placement guide, U.S. Pat. No. 6,415,167 (Jul. 2, 2002) describe a coupling fluid of one or more perfluoro compounds where a quantity of the coupling fluid is placed at an interface of the optical probe and measurement site. Perfluoro compounds do not have the toxicity associated with chlorofluorocarbons.
Calibration Adjustment
Several methods have been reported to compensate in some part for the dynamic variation of tissue samples.
One reported method of calibration model generation for noninvasive glucose concentration determination is to model an individual over a short period of time [see K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August, 1995); and J. Burmeister, In-vitro model for human noninvasive blood glucose measurements, Doctoral Dissertation, University of Iowa (December 1997)]. This approach avoids modeling the differences between patients and therefore cannot be generalized to more individuals. This approach also fails to address the prevalent short-term problem related to physiologically induced variation and no means of compensating for variation related to the dynamic water shifts of fluid compartments is reported.
Another approach to overcome the effect of tissue variation on a model is to use cross-validation. In one study, meal tolerance tests were used to perturb the glucose concentrations of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation [see Robinson M. R.; Eaton R. P.; Haaland D. M.; Keep G. W.; Thomas E. V.; Stalled B. R.; and Robinson P. L. Non-invasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin Chem 1992;38:1618-22]. This approach models the differences between some patients presumably with the intent of modeling variations so that future subjects are predicted by the original model. This approach also fails to address the prevalent short-term problem related to physiologically induced variation and no means of compensating for variation related to the dynamic water shifts of fluid compartments is reported.
Still another approach to overcome the effect of tissue variation on a model is to use extensive calibration of each subject through a series of glucose perturbations often over an extended period of time such as 2 to 12 weeks. Often these calibration periods are followed by an evaluation period during which a subject goes through one or more additional glucose excursions. The intent is to incorporate into the model an extensive set of conditions covering future conditions when predictions are made. When many excursions are used, this incorporation often occurs over a period of weeks. To date, this extensive calibration and testing protocol has met with limited success.
Yet another method to overcome the effect of tissue variation on a model is to compensate for variation related to the structure and state of the tissue through an intelligent pattern recognition system capable of determining calibration models that are most appropriate for the patient at the time of measurement [see Malin, S. F.; et. al. An Intelligent System for Noninvasive Blood Analyte Prediction, U.S. Pat. No. 6,280,381]. The calibration models are developed from the spectral absorbance of a representative population of patients that have been segregated into groups. The groups or classes are defined on the basis of structural and state similarity such that the variation within a class is small compared to the variation between classes. Classification occurs through extracted features of the tissue absorbance spectrum related to the current patient state and structure.
Still an additional group of approaches to overcome the effect of tissue variation on a model is calibration transfer. A number of pretreatment of spectral data techniques have been employed. A general but incomplete list of these pretreatment steps include trimming, wavelength selection, centering, scaling, normalization, taking an nth derivative (n≧1), smoothing, Fourier transforming, principle component selection, finite impulse response filtering, linearization, and transformation. This general class of techniques is found to be limiting in terms of noninvasive glucose concentration analyzer requirements.
Still an additional approach to overcome the effect of tissue variation on a model is a group of techniques based upon local centering using a single spectrum [see Lorber et. al., Local Centering in Multivariate Calibration, Journal of Chemometrics, 1996, 10, 215-220]. In this method, a spectrum is selected for mean centering the calibration data set that is the closest match (with respect to Mahalanobis distance) to that of the unknown sample spectrum. A separate partial least squares model is then constructed for each unknown sample. This technique does not reduce the spectroscopic variation of the calibration set.
Another approach to overcome the effect of tissue variation on a model is related to the technique of mean centering [see E. Thomas, R. Rowe, Methods and apparatus for tailoring spectroscopic calibration models, U.S. Pat. No. 6,528,809 (Mar. 4, 2003) and E. Thomas, R. Rowe, Methods and apparatus for tailoring Spectroscopic Calibration Models, U.S. Pat. No. 6,157,041 (Dec. 5, 2000)]. This method uses spectrographic techniques in conjunction with an improved subject-tailored calibration model. In calibration data, the model data is modified to reduce or eliminate subject-specific attributes, resulting in a calibration data set modeling within-subject physiological variation and instrument variation. In the prediction phase, the prediction process is modified for each target subject utilizing a minimal number of spectral measurements for each subject. However, this method does not address the key problem of short term physiological and chemical changes related to the dynamic nature of the tissue nor the intra-patient variation related to the heterogeneity of the tissue sample.
E. Thomas, U.S. Pat. No. 6,528,809, supra and E. Thomas, U.S. Pat. No. 6,157,041, supra use an infrared based noninvasive glucose concentration analyzer to obtain absorbance spectra of human tissue in combination with a model that is periodically corrected with the use of both a spectrum collected from the tested subject and an invasive reference glucose concentration determination collected from the tested subject. The technique employed is loosely referred to as mean centering, though the subtracted spectrum is not the mean spectrum and the glucose value is used to correct an offset in the predicted value. Collection of the reference spectrum is time consuming, requires some expertise on the part of the user, requires data collection software, and requires a microprocessor or other computing means to implement into the calibration. In addition, the acquired sample spectrum to be used as a reference spectrum contains data collection errors and has spectroscopic attributes not accounted for in the original model. Incorporation of the reference spectrum of the individual into the model thereby results in a significant potential source of error in the resulting calibration that directly translates into errors in subsequent glucose concentration predictions. In a noninvasive glucose determination, this results in an erroneous glucose concentration being reported that is used as an adjunct method for directing insulin therapy. For all of these reasons, elimination of the step of collecting and utilizing a reference sample spectrum is beneficial.
Guide
T. Blank, G. Acosta, M. Mattu, S. Monfre, Fiber optic probe guide placement guide, U.S. Pat. No. 6,415,167 (Jul. 2, 2002) and T. Blank, G. Acosta, M. Mattu, M. Makarewicz, S. Monfre, A. Lorenz, T. Ruchti, Optical Sampling Interface System For In Vivo Measurement of Tissue, U.S. patent application Ser. No. 10/170,921, (filed Jun. 12, 2002), which are both herein incorporated in their entirety by this reference thereto, describe use of a guide in conjunction with a noninvasive glucose analyzer to increase precision of the location of the sampled site resulting in increased accuracy and precision in a noninvasive glucose concentration determination. The guide is used for a period of time to increase precision in sampling throughout a period of sampling, such as a fraction of a day, one day, or a period of multiple days.
Equilibration
A number of reports exist describing the difference (or lack of difference) between traditional glucose determinations and alternative site glucose determinations. Some have recognized the potential difference as having impacts upon noninvasive glucose calibration and maintenance, see U.S. patent application Ser. No. 10/377,916. The use of heat, rubrifractants, or the application of topical pharmacologic or vasodilating agents such as nicotinic acid, methyl nicotinamide, minoxidil, nitroglycerin, histamine, menthol, capsaicin, and mixtures thereof to hasten the equilibration of the glucose concentration in the blood vessels with that of the interstitial fluid has been reported. [see Rohrscheib, Mark; Gardner, Craig; Robinson, Mark R. Method and Apparatus for Non-invasive blood analyte measurement with Fluid Compartment Equilibration, U.S. Pat. No. 6,240,306, May 29, 2001 and Robinson, Mark Ries; Messerschmidt, Robert G. Method for Non-Invasive Blood Analyte Measurement with Improved Optical Interface, U.S. Pat. No. 6,152,876, Nov. 28, 2000].
Release of nitric oxide via photo stimulation is described for use in combination with noninvasive glucose determinations as a method of equilibrating glucose concentrations in poorly perfused regions of the body with glucose concentrations of more well perfused regions of the body. [T. Blank, S. Monfre, M. Makarewicz, M. Mattu, K. Hazen, and R. Henderson, Photostimulation method and apparatus in combination with glucose determination, filed May 6, 2004.
In all of the related technology of this section, no suggestion of the use of mean centering utilizing only a reference glucose value in conjunction with a guide that eliminated the need for a spectral reference is made. Further, no suggestion is made for easing the use of a bioanalyzer such as a near-IR based noninvasive glucose analyzer through the use of a guide to reduce the need for mean centering related techniques based upon spectral references. Further, no minimization of reference glucose concentration differences has been suggested with the use of photo stimulation. Finally, to date no FDA device has been approved for the utilization by an individual or a medical professional for noninvasive glucose concentration determination.
The Problem
Physiological parameters that change the state of skin include: tissue hydration, skin temperature, volume fraction of blood in tissue, skin thickness, magnitude of absorbance features related to fat, hematocrit concentration, and surface reflectance. Many of these parameters change over a period of one or more days or over a much longer period of time such as weeks.
Changes in the state of skin alter a number of properties such as: water concentration, the concentration of other analytes such as protein, fat, keratinocytes and glucose, the scattering of skin, the absorbance of skin, the refractive indices of various layers of skin, the thickness of tissue layers, the emitted radiation from the body, the mechanical properties of tissue, magnitude of absorbance features related to water, magnitude of absorbance features related to protein, and the size and distribution of scattering centers.
Noninvasive spectra, such as a near-IR based diffuse reflectance spectrum, are representative of skin tissue properties. Since a large number of state changes each effect a large number of skin tissue properties, variations through time of noninvasive spectra of a given skin sampling site often vary in a highly nonlinear and profound manner. Further, factor analysis based multivariate models result in abstract features. Therefore, change in state often profoundly effects multivariate model predictions.
Because near-IR based noninvasive glucose analyzers typically use multivariate analysis that is susceptible to sample state changes, the model must be robust or optionally updated. The invention herein focuses on maintenance of one or more calibrations. Calibration maintenance is a costly and time-consuming process that requires some technical skill. Elimination or automation of steps required for maintenance of a noninvasive glucose analyzer is beneficial for at least one of increasing marketability of the analyzer, increasing the number of people who may use the analyzer, reduction in time requirements associated with a glucose concentration determination, and increased precision and/or accuracy of a glucose concentration determination. Specifically, elimination of any data gathering step, such as collection of spectra, is beneficial for the above reasons. This invention provides a simple calibration maintenance method for use with a noninvasive glucose concentration analyzer.